基于非均衡序贯设计的大规模关键质量因子筛选研究  

Key Quality Factor Screening for Large-Scale Simulation Based on Unbalanced Sequential Design

作　　者：刘丽君 马义中[2] 龚本刚[1] 吴锋 唐丽娜 Liu Lijun;Ma Yizhong;Gong Bengang;Wu Feng;Tang Lina(School of Economics and Management,Anhui Polytechnic University,Wuhu 241000,China;School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China;School of Economics and Management,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]安徽工程大学经济与管理学院,安徽芜湖241000 [2]南京理工大学经济管理学院,江苏南京210094 [3]江苏科技大学经济管理学院,江苏镇江212100

出　　处：《中国管理科学》2025年第2期84-94,共11页Chinese Journal of Management Science

基　　金：国家自然科学基金项目(71931006,72471003)。

摘　　要：随着系统复杂度的提高,涉及到的因子数目越来越大,采用尽可能低的试验成本识别出显著影响质量特性的关键因子是质量改进活动中的重要环节。针对大规模关键质量因子筛选中样本有限性、试验经济性以及数据的非均衡特征等问题,本文提出了基于非均衡序贯设计的大规模因子筛选方法。首先,构建同时考虑因子的位置及散度效应的一阶模型,并且结合序贯分支方法的基本假设及框架,提出了综合应对两种非均衡数据的大规模因子筛选方法 SB-UB;然后,针对两种非均衡数据类型,分别提出改进的Bradley-Blackwood方法以及融合F检验以及学生t检验的双重检验法,同时检验因子(组)的位置及散度效应;最后,采用蒙特卡洛仿真试验说明所提的大规模因子筛选方法 SB-UB的有效性及稳健性。As system complexity increases,identifying key factors that significantly affect quality characteristics with minimal experimental cost becomes a critical step in quality improvement activities.However,challenges such as limited samples,economic constraints of experimentation,and the presence of unbalanced data neces⁃sitate the development of new factor screening methods.A large-scale factor screening approach based on unbal⁃anced sequential design is introduced to address these issues.First,a first-order model that simultaneously considers both location and dispersion effects of factors is constructed.Integrating the fundamental assumptions and framework of sequential bifurcation(SB),the SB-UB method is proposed to handle two types of unbal⁃anced data.For the first type of unbalanced data,an improved Bradley-Blackwood test is introduced,while for the second type,a dual test combining F-test and Student’s t-test is proposed.Both methods aim to examine the significance of the location and dispersion effects of the factors.Monte Carlo simulation experiments demonstrate the effectiveness and robustness of the proposed SB-UB method for large-scale factor screening.By incorporating both location and dispersion effects,this approach enhances the accuracy of identifying critical quality factors while maintaining a low experimental cost.To validate the method,several simulation experi⁃ments,including large-scale simulation systems such as supply chain models,are presented.These systems often involve dozens or even hundreds of factors,far exceeding the capabilities of traditional factor screening methods,which are typically designed for problems involving fewer than 20 factors.The proposed method allows for effective screening even when unbalanced data,due to the use of multiple devices with differing computational capacities,impacts the quality of experimental results.The primary results of this research contribute to the field of quality improvement by providing a robust method to identify key factors under ch

关 键 词：非均衡设计 大规模因子 因子筛选 序贯分支方法 散度效应 

分 类 号：F273.2[经济管理—企业管理]